class_docstr: A class representing a discrete probability integral transform of a given
  quantile-based learned binning.  Note that this representation is intended
  to be applied on top of a quantile-based binning representation.
  
  Parameters
  ----------
  num_bins
      Number of bins used by the data on which this representation is
      applied.
  mlp_tranf
      Whether we want to post-process the pit-transformed valued using a MLP
      which can learn an appropriate binning, which would ensure that pit
      models have the same expressiveness as standard quantile binning with
      embedding.
      (default: False)
  embedding_size
      The desired layer output size if mlp_tranf=True. By default, the
      following heuristic is used:
      https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html
      (default: round(num_bins**(1/4)))
code: def hybrid_forward(
      self,
      F,
      data: Tensor,
      observed_indicator: Tensor,
      scale: Optional[Tensor],
      rep_params: List[Tensor],
      **kwargs,
  ) -> Tuple[Tensor, Tensor, List[Tensor]]:
      data = data / self.num_bins
      if self.mlp_transf:
          data = F.expand_dims(data, axis=-1)
          data = self.mlp(data)
      return data, scale, rep_params
  
file_path: gluonts/src/gluonts/mx/representation/discrete_pit.py
imports: from typing import List, Optional, Tuple
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .representation import Representation
label_desc: Use image node_id 2 for calling the DiscretePIT obj's underlying member method code with example usage: obj.hybrid_forward(F, data, observed_indicator, scale, rep_params) and returns: data,  scale,  rep_params